A hybrid PSO-parallel fuzzy brain emotional learning classifier for medical diseases diagnosis

Article type: Research Article

Authors: Sun, Yuan | Lin, Chih-Min^{; *}

Affiliations: Department of Electrical Engineering, Yuan Ze University, Tao-Yuan 320, Taiwan

Correspondence: [*] Corresponding author. Chih-Min Lin, Department of Electrical Engineering, Yuan Ze University, Tao-Yuan 320, Taiwan. E-mail: [email protected].

Abstract: This study presents a fuzzy brain emotional learning classifier (FBELC), combined with a modified particle swarm optimization (PSO) algorithm, that allows a network to automatically determine the optimum values for a reward signal and a classification threshold. The designed FBELC model imitates the brain decision process including the emotion information. To verify the predictive performance, a novel fitness function based on the accuracy of the training and cross-validation datasets is used for a PSO algorithm. This PSO-FBELC model is used to diagnose breast tumors and heart diseases. A comparison of simulations using the proposed PSO-FBELC with other processes shows that the proposed model performs better in terms of recognition accuracy.

Keywords: Fuzzy brain emotional learning classifier (FBELC), particle swarm optimization (PSO), disease diagnosis

DOI: 10.3233/JIFS-201418

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7953-7960, 2020